Background Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.

Objective The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.

Methods We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.

Results We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

背景 行为和健康有着千丝万缕的联系。因此，连续的可穿戴传感器数据提供了预测临床措施的潜力。但是，数据收集会发生中断，这就需要进行战略性数据插补。

目标 这项工作的目标是采用数据生成算法来插补多元时间序列数据。这将使我们能够创建可以预测临床健康措施的数字行为标记。

方法 我们创建了一个双向时间序列生成对抗网络来估算缺失的传感器读数。对于单个时间点或较大的时间间隔，根据多个字段和多个时间点之间的关系估算值。从完整数据中提取数字行为标记并将其映射到预测的临床指标。

结果我们使用n  = 14 名参与者 的连续智能手表数据验证了我们的方法。重建遗漏数据时，我们观察到平均归一化平均绝对误差为 0.0197。然后，我们创建机器学习模型，以根据相关性从r  = 0.1230 到r  = 0.7623 的重建的完整数据预测临床指标。这项工作表明，在野外收集的可穿戴传感器数据可用于提供有关自然环境中人的健康状况的见解。